Most existing work in few-shot segmentation uses variants of branched CNN architectures[1]}, [2]}, [3]}, [4]} or relational networks[5]}. In general these methods rely on using learnable modules to find the similarity between support and query images. Although meta-learning has seen a lot of success with few-shot image classification, frequently reaching state-of-the-art results, these techniques have not been as extensively used for segmentation. Some examples include [6]}, [7]}. We use the FSS-1000[8]} dataset for our models. This dataset is designed specifically for few-shot segmentation. Most previous works made use of the PASCAL-5i dataset[9]}, which is a few-shot version of the PASCAL-VOC dataset[10]}. PASCAL-5i only has 20 classes, which is far lower than FSS-1000. Li et al. argue in [8]} that approaches trained on only 20 classes may suffer from strong bias. A drawback of this dataset is that most images contain a single object. Because of this, we have specifically visualized some test images that contain multiple objects in Figure 4.
